Spoiler: Only valid reasons for humanity’s survival are:
1) We are human, so it matters to us
2) Will be kind of sad if we never get something like humanity again. If humans die out, there is a non-0 chance of never seeing anything like humanity again.

Most authors who write essays on humanity's survival, start with the premise that it does matter, and then continue to explain why it's the case. Here, I question that premise by starting with the most common arguments for our survival.

Argument 1:

The objections to this are simple. We do not know that either:

1. We are the only species

2. We are currently the only species

Re. (1): Perhaps we are merely the first to reach this stage, and in a few million years, another species will follow. Life on Earth has existed for billions of years, while human civilisation is only a few thousand years old. In the grand cosmic timescale, our entire existence is a mere blip. Does the temporary presence of one intelligent species on one planet truly hold universal significance? Or is this just another species overemphasising its importance?

However, the counter-argument to this 'cosmic blip' theory focuses on our future potential, positing that in a vast, silent universe, we are the only known life capable of carrying consciousness beyond this planet. If we fail, that opportunity might be lost forever. However, this assumes human creativity is an endpoint, not a stepping stone. It is entirely plausible that a successor intelligence could create art and science far surpassing our own, making our library not unique, but merely foundational.

Re. 2: On the other side, we don't know that animals aren't conscious. We don't even know what consciousness is. Consciousness relies on a certain je ne se quoi. You know it when you feel it. At this point in time, we're defending the need for something when we don't even know how to define it.

Argument 2:

A simpler, more tribal argument is that:

I get this, but this seems slightly primitive in our understanding. Dinosaurs also would've cared that dinosaur's survival matters. If a meteor was an extinction-level event for dinosaurs, is a self-created AI an extinction-level event for us? From a neutral, cosmic perspective, is there a difference?

Other Arguments

Beyond these, other sophisticated arguments for our survival exist. One centers on the irreplaceable value of our creations. Even if another intelligent species evolves, will they compose Beethoven's 9th Symphony or devise the theory of general relativity? This view suggests that losing humanity means the universe loses a unique library of art and science forever. Do I agree, probably not, have you tried Elevenlabs new music model?

A final, more pragmatic argument is that our survival matters less to prove our own cosmic significance and more to prevent future suffering. A world after us, particularly one dominated by a misaligned AI, could be a realm of pointless suffering, and from this perspective, our existence is a necessary guard against that potential dystopia.

Given these deep-seated doubts about our objective cosmic importance, we are left with a final, more personal question. From a purely objective standpoint, humanity's survival may not matter. However, it matters subjectively to us. The end of humanity feels bad. This subjective value, while not cosmically significant, provides a perfectly rational basis for managing existential risks like AI with extreme care.

My experience

I’ve deployed actively into funds over the past 3 years, most notably into the funds Upside, Firedrop and Intuition. Sadly I didn’t meet the minimum for Concept’s fund 3 years ago, but will be going into their fund II. At the same time, I’ve passed on dozens of other funds - because most funds don’t perform.

I actively pursue 3 strategies when putting my (micro) cheques into these funds.

1. Growth funds that can access hot deals. There is a literal two tier world when it comes to funds, with the top ones having the pick of deals on the hottest AI and defence startups that are virtually guaranteed next rounds, and everyone else. If you can get into hot deals in hot funds, do.
   I do this through syndicating funds and using creator leverage to get into deals. If you’re interested in accessing these or joining a syndicate, hit me up.

2. Emerging managers: these managers are often hungry and can access deals once the rounds are almost closed by virtue of the value they add relative to ticket size. They are statistically generating better returns and a lot of LPs are super keen to get in.

3. Creator economy funds. Also as discussed below, creator funds win on deal sight and access, even if they lose on diligence - in this market following hot rounds is near enough a good strategy.

4. SEIS/Tax efficient funds (in the UK) - their deals are rarely exciting, but they need to return 0.3x for you to be in profit so the punt seems worth it.

Here’s what I look for when I review a fund deck

• ·Macro - are they investing into a macro trend that you believe in (i.e. is the sector growing or expected to grow fast according to you)

  • On thesis - do they make a thesis and stick to it / is there consistency in their portfolio or do they just spray and pray?

  • Think about region too here (also currency risk)

• Micro - have they got a good deal by deal track record

• Three parts to being a good VC: Sight, access and diligence

  (You want to make sure that a fund achieves 2 out of these 3)

  • Sight: = do they see a lot of good deal flow

  • Access = what proportion of deals that they seen do they win - do they have a right to win deals that others don’t

    • Based on ticket size relative to value add (also visible by track record)

    • This can be done because they are a large fund and have a great reputation so can win rounds or if need be slightly outbid

    • Or better if they’re a small fund with extreme value add so they get into lots of deals

  • Diligence = can they tell good deal from bad. Do they have the ability to identify opportunities where others do not

    • This helps as you can get into rounds others do not believe in (and therefore get in at lower rates)

    • This also helps as you’ll have fewer misses

• Specific things:

  • GP contribution = apart from for first time managers, this should be >5%, if not the skin in the game isn’t the same and they can be incentivised by the management fees

  • Fees = 2:20 as standard over a 10 year fund cycle

  • 1x minimum before carry is split = better if there is an 8% a year hurdle or the like

• Other stuff:

  • Best funds:

    • Imo = the best funds are small funds, emerging (first-time) managers with deep expertise in a specific area, as they can tag into good deals and are accessible to institutional investors

    • If you can get access then large funds that lead good rounds, but really here we mean Sequoia, Founder Fund, A16Z, Lightspeed, Bessemer and not many beyond that

  • Deck = if you can’t explain it simply, then you haven’t really understood

    • Anything too complex likely doesn’t make sense - a good fund manger will save that for the pitch

  • Team

    • Show don’t tell = what is your overall angel track record - not just a few winners

    • Have you built a good company before or learnt the ropes somewhere good

    • Do I sit in a room with you and get impressed - are you formidable https://paulgraham.com/convince.html)

    • Do they have bullshit jobs in their fund?

    • Does the team look meritocratic - are they properly incentivised?

    • Are the team by any metric super good - talent attracts talent and founders - if they team aren’t epic, their deal flow will be mid

    • Do not fall for experience - Venture fund partners who’ve already made their cash can often take up too much of the profit for doing none of the work, disincentivising the whole team (even if the team is overpaid, they will only look at their pay relative to the other people in the team and respective workloads)

• Other LPs - can be a good indicator, but don’t let it be the guiding force

• Strategy / number of deals:

  • They should be able to articulate a simple and sensible strategy that goes through sight, access and diligence so you can understand where they are on each

  • They should be doing (in my view 60 deals

  • Across 4 years - longer is likely better - remember you’re making an annual commitment for a set period of time as opposed to a lump sum commitment

• Risk appetite - in all cases, higher risk also correlates to higher potential reward

  • Stage - early stage is riskier

  • Number of deals - fewer deals = more risk

  • Timeframe = short is risky - for this one I’d actually ensure a 4 year minimum investment period to prevent getting tucked into bubbles

• Government support

  • If there’s Government money that matches or backs up your money (e.g. EIS/SEIS) then that can hugely amplify returns - would generally always do these funds)

• Friends

  • I find with close (real) friends they won’t take your money unless they’re serious. I’ve never lost money investing in a friend (even when I didn’t believe in their company) - and I’ve only done it twice, but those are my top 2 portico’s - which probably says a lot

• Note

  • VC is all about outsized returns - so if your fund is not top quartile you’re in effect losing money - so really think about if the fund is top quartile on a combination of the 3 factors I raised earlier - sight, access, diligence

    

There are 2 opposing ways IP rights will determine the future of content clipping, and I believe which reality you have to adhere to will depend on Geography/Jurisdiction.

All to say that content clipping will exist in it’s freest form at least in some jurisdictions.

Viewpoint 1: IP is an illusion

This is the view endorsed by Jack Dorsey, Elon Musk et al.,

This reality will pervade in large parts of the world who care little for IP law, and for the long tail of creators for whom it will be tough to enforce IP law even if it is banned.

Viewpoint 2: IP is real

This stance will last in particular in areas where the cinema and music lobby is strong, but again enforcement is difficult.

In this case there are 2 reasons why a viral content clipping system still retains insane value:

A) Creators will pay you to get them eyeballs - long term so will brands and other sponsors

B) Content generators will license their content to editors for a share of upside - e.g. leveraging Story Protocol

1. Every VC that passed overindexed on TAM, competitor threat and the lack of an engineer as a founder. all massively under indexed on how individually exceptional (as humans) the team were.

2. The team ran fast and hired people who were both running obsessed and had built similar functions before, as opposed to brand names on CVs.

3. SEIS is a gold mine that has massively inflated the returns early investors received by close to 4x… get in!

4. Venture is hard: Eka got solid terms on what was an entirely uncompetitive deal for them, this is the breakout success that most VCs can only dream of, yet it’ll likely only generate them 25x (based on the Times’s 30x estimate), which barely covers half of their fund. That said for everyone one of these deals they score, they can afford to have 8 fails and still 3x the fund. So you can decide whether picking startups is hard. That said, keep a close eye on the creator ventures strategy of passing on first rounds and getting in as they see traction, and doing 5-6x in 1.5 years.

5. Some from the sidelines say the founders sold early, that American VCs or founders would rarely have taken an exit when they had all the momentum. There may be truth to this, but those people are usually on the sidelines for a reason, and most of them passed on the deal… so… they can try to give this argument more weight when they too build and sell a company for 9-figures.

One of the most amazing things about Runna, is how in awe literally everyone at the company is at every new tier of success that they achieve.

From first round, to Forbes, to the acquisition, they’ve lived this business like kids in a candy store - a profitable one too.

First intros

I first properly got to know Dom, the running obsessed founder of Runna, when I was interviewing to move to his product team at McKinsey. I left with little view other than, he’s great vibes, and would be fun to work with. At the time I was deciding between working in his product team, or doing a secondment to McKinsey in Japan, and he very openly made his case for why being the first member of his team would be great, especially as a precursor to founding; but he was also clear that there was no pressure to take the role given that a secondment in Japan sounds pretty epic - I ended up doing neither, but the person who went to Japan in my place met the partner of their dreams and has moved their permanently so…

Discovery and first round

Months later, I saw on LinkedIn that Dom had left the firm (as we call it) to found so I dropped him a LinkedIn messaging offering to help in any way needed, later sending across VCs and Angels for him to speak too.

Of everyone I pointed out or sent the deal too, only one invested, a well know Partner at McKinsey who put in a £50k cheque that based on public estimates he’s 30x’d (but note due to SEIS he’s actually 109x’d his max loss), all capital gains free.

Everyone else passed. A friend who runs a well reputed fund in London refers to how this was one of his few major misses, as the founders had the optimal backgrounds according to their founder profiling methods.

In Dom’s LinkedIn post announcing the deal he refers to how they pitched 100 VCs and they received 1 term sheet.

There were several reasons for this (in my view):

1. Dom didn’t have the excessive public overconfidence (public school arrogance) that VCs love - he instead has an insane work ethic and great management style

2. The founding team had a running coach (albeit a tier 1 running coach) and a product CEO. Not the classic CEO CTO grouping. VCs as much as they say they back rebels, love patterns. Even VCs who talks about how they break the mould will assess based on a similar people model.

   1. In hindsight, this CEO and expert/creator/specialist combo seems like it should be the go-to app model in a world where tech is becoming increasingly commoditised.

   2. What’s also crazy is how many of these consumer apps were first built by dev shops after they were thought up by non-technical founders: Uber ($156bn), Linktree ($1.3bn), Calendly ($3bn)

3. Nobody believed in the potential market for the business. In some ways it felt like 2 school friends building an app - and almost all mobile apps fail. It didn’t feel like a VC backable business (more on this at the end)

Even when I invested it was purely in wanting to support a friend and founder who I rated, to this day I haven’t claimed any of the run plans or rewards that the crowd fund investors received. I hate running. I had no way to evaluate and no conception of a running app, but Dom was vibes, so I went in. In my existing angel track record, the only companies that have delivered solid consistent returns have been the friend cheques, and also the athletes - people who grind through the sh*t to get to the gold. The other one namely Chris Kong of Better Nature. I invested there 6-7 years ago, and he’s still going strong through all the sh*t that is running a consumer good startup.

Every VC I’ve spoken too since has said they underweighted the importance of the founder. If they’d just judged them on how exceptional they were as founders, the story would have been different. But it takes most to miss for one to win.

https://www.linkedin.com/feed/update/urn:li:activity:7318505721640189952/?commentUrn=urn%3Ali%3Acomment%3A(activity%3A7318505721640189952%2C7318510080021147648)&dashCommentUrn=urn%3Ali%3Afsd_comment%3A(7318510080021147648%2Curn%3Ali%3Aactivity%3A7318505721640189952)

That winner was, Eka Ventures who led the £1.9m seed round at around £6m pre-money valuation (based on backsolving). This happened the same quarter where I recall Runna was already doing strong monthly recurring revenue. None of the major funds went into the deal and the angel list was primarily creator focused.

Scaling and Series A

As the company scaled at an INSANE pace, I remember seeing the hockeystick, churn numbers and everything else, they quickly hit an insane monthly growth rate and revenue number - which I’m not at mercy to disclose. They then raised the next round at an unbelievably attractive valuation (c.£5m at a c.£30m valuation).

At that point, I was scaling the most viral content studio in the world, from which we spun up a creator VC fund - JD ventures. Using the leverage of our studio we were one of a few creator funds who participated in this hugely oversubscribed round and were only able to get a quarter of our requested allocation given how in demand the round was.

We deployed >5% of our fund into this deal, and in the process have returned a large chunk of the entire fund within 1.5 years of this investment. As far as I recall, this deal was done via a DM by Julius to Dom, then a short call with our team, and a relatively quick whatsapp back and forth between myself and Dom to agree on allocation and get the deal closed - super fast.

This round, led by JamJar, was followed on by a several other funds including my favourite fund Creator Ventures and Sasha Kaletsky, who have a superb strategy of using creator pull to get into consumer rounds, and always seeing deals early and then watching them until their next rounds, picking the winners (as they have done with Runna, ElevenLabs, Praktika and I’m sure many more).

Hypergrowth: the value of obsession

After this round, I performed a magic show at the Runna summer party at Dom’s house, and again could see this familial vibe, running obsessed. Unlike most corporates or startups, the team all gelled like a family, and had a great time working and playing!

I later also performed at their Runna Ambassador away day, and left feeling nothing but an obsession that everyone around me had with running. As someone who never even filled the forms for the free run plan that investors were offered, I obviously didn’t fit, and left the dinner as soon as food was done as I wasn’t sure I couldn’t do another conversation about running: In the greatest way! This was another part of the secret sauce. EVERYONE being obsessed. Literally EVERYONE who worked there, or was in any way tangentially involved was obsessed with running.

People loooovveee running… like I’m doing 5ks on lunch break cos that’s just what people do here - [friend who recently joined at Runna]

Obsession trumps all. From what I recall, beyond being more than a 9-5, the company didn’t seem to have an intense 9/9/6 culture that they US startups pride themselves on, they did have truly obsessed teams working for the good of the company.

Hypergrowth: hiring obsessed specialists

During this phase I’d often share vacancies with friends and at points considered applying myself. What I found was that they’d very quickly start hiring specialists, but specialists who were in some way obsessed with running - whether as beginners getting into it, or regular runners. Looking at their profiles, unlike Dom who was a generalist from a brand name firm, the majority of the rest did not come from large brand name organisations, but they’d built the same function at another org. This followed the classic Airbnb strategy of find people who’ve built the same product elsewhere and do whatever you can to hire them. Below is an example of a member of the growth team, who’d previously run a fitness app.

Start with the results and work backwards to the people.
Most people start with resumes, they start with brands, but you should actually ask yourself what products do I admire, and then who built those products, and who actually built them. It’s like a detective novel to find out who built them.

- Brian Chesky (AirBnB CEO)

And then came the exit - and to nobodies surprise Strava went in… my tiny cheque has returned me more (tax free) gains than any money I saved during my years at McKinsey, and an given me an even better adventure.

What could have been

There is always going to be the other side of the acquisition where investors, the team, the spectators ask what could have been.

According to the Times, Runna returned c.30x to their earliest investors (to SEIS investors this was substantially higher but we’ll go with 30x). As one of the break out startup success stories in the UK, this investment won’t even be a fund returner for it’s largest investor shareholder.

It could be argued that if they had been in America, or had US investors on their cap table they may have not gone for an acquisition and instead try to outcompete Strava.

By the end of it, due to the level of dilution in their rounds, the founders held under 45% combined, maybe there was no incentive to take this further when they could sell and then get ready to do this all over again in a few years (this time raising at a crazy valuation from the get go).

To this I’d argue, “🤷”, two underestimated founders from the UK have just built from scratch (within 4 years) a company that has been acquired for 9-figures. Most people (including me) were just watching from the sidelines, it’s a fair point but not one many of us can dive into until someone offers us 9-figures for a company that was a mere idea 5 years ago.

Huge congrats to Dom, Ben and the entire team on this crazy adventure… onto the next part of it!

Note from me

I’ll be pretty actively deploying during this phase, so let me know if you either have an epic deal to pitch, or would like to angel invest and syndicate deals, please get in touch with me on LinkedIn.

I’ll be writing a tonne here, so please subscribe if you’re interested.

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Reflecting back on my career so far, I’ve had numerous ups and downs, and I regret none of them. I’ve made active decisions to take substantial risks early on in my career and chosen to pursue more novel experiences at the expense of ensuring that those experiences are good. The only times I do regret are the ones where I have been passive – whether for days, weeks or months.

Being passive is often a result of trying to do too much at once and in turn making no progress on any of your goals. It is also a product of being around people who behave passively. Passivity is in my view the single driver of stagnating your career, and the only thing I actively discourage you from doing.

Introduction

Over the past 9 years, I have had an active say in how my time was allocated. From the ages of 16-18 I opted to go to sixth form and sit my A-levels in Maths, Economics and Chemistry. From 18-21 I studied Psychology & Philosophy at Oxford – whilst continuing to commit around 20 hours per week to my magic career, and the summers to exploring different potential careers – some paid and some unpaid. Post that I spent several months exploring creative magic opportunities in India before beginning a 2.5-year stint at McKinsey. Most recently, I’ve spent 1.5 years at a web3 start-up, taking the company as employee #1 with 3 part time founders, through a $10.3m raise and to a team of 12 people. At this point in time, I have decided to relinquish formal work to get deeper on the future impact I’d like to make: leveraging new technology in creator economy and India.

In this process of multiple active learning experiences, jobs, and periods of free time; as well as through working on side projects and learning opportunities throughout my career, I’ve come up with a framework for how I assess career, learning and other opportunities framed around three core time use categories.

The reason the split is so useful is that it ensures an active thought process in how time is used, rather than resorting to defaults.

3 Uses of Time: Learn, Earn, Live

I’ve split my time uses into learn, earn and live. All three of the categories directly impact one another over the long term and all opportunities will provide a different balance of each, both at the start of a career and as you progress.

Learn

There are two core types of learning: active and osmotic

When assessing the value of active learning in a potential opportunity, it’s worth assessing how much that learning or qualification will have in the type of opportunity you will then have available to you.

Accountancy firms often realise that your value goes up substantially post qualification so save their large internal pay bumps until then, or alternatively require you to pay back “fees” if you leave within 2 years of qualifying (charging you for the active learning you receive).

As you go through life and your career, learning goes from centred around active learning to centred around osmosis learning. Osmosis learning is much more subtle – whether it be learning how to behave in a meeting, how to negotiate, how to communicate concisely. The best way to measure the likely level of osmosis learning you will receive is based on:

1. Quality of people you work with: great people attract and grow even more great people

2. Time Spent: with these great colleagues

3. Progression: Quality improvement of other people at said potential employer from the start to when they leave a role

4. Feedback culture: at said organisation – regular feedback from people previously in your position allows you to identify core osmotic learnings picked up or for the creation of more situations of higher osmotic learning.

The best learning combines both active and osmotic learning with active being used to grasp core concepts and pieces of knowledge and osmotic to pick up the nuances that enable you to better leverage that active learning.

Learning in all forms compounds:

As learning compounds, optimising for it is more beneficial particularly early on in your career or in a field. Moreover, with the exponential growth of technology, ability to leverage new tooling becomes more valuable. Graduates today will likely need to reinvent their competencies multiple times over the course of their careers.

Learning is more controllable early in your career as:

• At earlier career stages you tend to have more time outside of work which can be used for active learning

• Colleagues see the value of you doing learning early as the results are more visible on your performance and therefore their workload

• Expectations on more junior employees are generally lower as they are paid less

As such, optimising for learning early on in your career is a great move.

Returns on learning diminish with time in most organisations despite compound benefits

As such, to ensure optimal learning you often have to search for it through exposure to more novel scenarios or otherwise. A good heuristic to use is that you can generate most of your active learning in a job in a year. After that year your skills should open you up to more osmotic learning opportunities, which also taper off after a year. So a 2 year window is often seen as an appropriate time in a role.

Earn

There are three core components to earning:

• Net Adjusted Compensation

• Signalling: Credibility/Credentials

• Connections

In terms of compensation it's important to note compensation post tax, and some form of cost of living adjustment, and then determine a net adjusted compensation.

Net Adjusted Compensation

This is the net (post-tax) amount that you earn, adjusted for cost of living (i.e. the amount you’d save per month), likely with some additional adjustment for lifestyle.

Cost of living adjustment: the adjustment needed to account for differences in living cost in different regions. Major cities like New York are generally more expensive than suburbs or developing regions. There are several calculators online to help you adjust for this.

Lifestyle adjustment: the expected change in lifestyle associated with living in a different region. People are aware that if they move from working in India to London they will likely not have any or as much domestic help.

I break down compensation, particularly for start-ups in more detail in [this post to be released soon].

Signalling: Credibility/Credentials

The second form of earning is around signalling value – the credibility and credentials that doing a certain job or task will earn you.

Signalling in most careers comes from three areas:

• The bar for getting into said company

• Skills that people expect that you have learnt by virtue of having done a certain job

• Any qualifications you can gain through doing a certain role (e.g. ACCA).

Large corporate names, such as Goldman Sachs, McKinsey or Google churn out enough graduates that relevant future employers generally know the bars that employees of these firms have already jumped, and the skillsets that these graduates possess after training there. Similarly, other more niche but respected entities such as Bridgewater, or Jane Street have smaller graduating class sizes but are also known for notoriously high entry bars and graduate growth prospects, which again leads to strong signalling value on exit (should you choose to leave – more often smaller reputable firms do everything possible to retain talent).

The value of signalling helps on multiple fronts, from making it easier to establish credibility in cold emails or other application processes. It often also increases the floor on which you’d land if you took a career risk that did not pay off. This enables you to take more risk.

Connections

Alongside providing signalling value that makes it easier to establish credibility, the connections that come from different roles are also a form of value that should be accounted for.

A substantial proportion of the job market is off grid – circling via individuals or recruiters. Smaller companies often don’t have specific roles that they hire for and instead just hire individuals based on their availability, proximity to the problem and desire to join the company.

This is perhaps one of the greatest differentiators between large and small companies. Where smaller companies will have fewer people with identical or similar functions, and therefore likely personality types, and future role / life ambitions; larger companies, especially professional services firms, have lots of similar people with similar ambitions.

As such; though being at a smaller firm may give you more exposure to senior or external stakeholders as you showcase your specific expertise, larger firms allow for you to work with a large group of stakeholders. This is beneficial throughout your career.

My first job in management consultancy has an alumni dashboard, and the London prior and current analysts all have a whatsapp group on which we asks all sorts of random questions – many have gone on to found incredible startups so having a group of them to bounce ideas around with is invaluable; many others hire or send job opportunities to the group.

When I left McKinsey, to enter the world of Web3, after meeting a fellow alumni member in an investors community, we set up a group (DAO) specifically for former McKinsey consultants in Web3. This group now exceeds 200 members, from partners at the firm, to execs at unicorns, and numerous top tier web3 founders, operators and investors. Working at the firm shows a certain pedigree in getting in as well as training through tenure; more interestingly, everyone is 1 or 2 degrees away from you so can very easily vet (due diligence) you to assess the merits of working with you.

Combine that with the clients, senior firm members, and suppliers you work with, you build an extensive network which means at the very least, you often have a couch to crash on when you travel as a broke startup founder.

A quick note on re-baselining

When switching jobs, compensation is often determined as a premium to existing compensation. There are a few roles for which there are more standardised pay packages:

-       More established institutions or schemes with more structured pay and progression scales also provide an opportunity to re-baseline as they are less likely to take prior salary into account when determining compensation.

-       Similarly, jobs which are substantially different, or job moves post a training scheme like an MBA also allow you to re-baseline to a higher core number. Geographical moves also provide similar re-baselining opportunities due to cost of living and comparables changes.

As such, if optimising for pay, it may make sense to drop into one of these roles to allow you to re-baseline effectively.

Live

Ability to “live” is defined by both the quantum and ability to leverage that live time. Leverage of time is the ability to use the off time to pursue what you intend to do with that time. The factors that affect time leverage are:

• Structure: do off hours enable you to do what you want with your off time

• Quality: physical and mental state during that off time

Ability to leverage raw hours depends entirely on what you intend to use the time for. The most common uses of this time are:

• Spend time with friends and family

• Learn a new skill

• Develop a side hustle

• Other things that give you joy - e.g. travel

If your objective is to leverage the time you have to learn a new skill or start a side hustle, an optimal scenario would likely be where you have off hours that can fit with your class or side hustle needs, and the energy during this time to pursue those activities with intent.

If your objective is to travel, then even if low intensity shift or on call work gives you lower hours and lower intensity than another job, it likely doesn't let you book holidays or long stints so easily. However, a high intensity project based job which lets you take more substantial breaks in between likely better fits those needs as you can have high quality time off during the off stretches, even at the compromise of lower quality time off during work stretches due to job intensity.

Case study: A lot of consulting firms noted this and now offer additional annual unpaid leave for people to pursue these opportunities. Though you may have less high quality time whilst you're working due to the hours and intensity of the job, access to stretches of unpaid leave to pursue travel goals makes that off time higher quality.

As detailed below, ability to leverage time dramatically reduces with aging, so though financial prudence and compounding is emphasised early, not making time for living whilst young is one of, if not the greatest regrets people face later in life.

Additional Thoughts

Making empty time the default

If you are comfortable that if you have an empty window of time, you will be able to effectively channel that time into good earning, learning, or living; then you are at an extreme advantage as you can optimise for when opportunity arises and be more patient in finding it.

The two core methods to help make empty time the default are:

• Keeping burn low

• Productively channelling empty time

Keeping Burn Low

As Sam Altman noted, money may not buy happiness, but it does buy freedom—and that's a game-changer.

For many, the primary motivation behind work is immediate financial need – if this isn’t you you’re privileged… embrace it.

Ample evidence shows that delaying earning for early learning can lead to greater financial success. Immediate financial needs often constrain our ability to pursue learning or living early in our careers emphasising the benefits of keeping personal burn low.

Side Note: Curiously, there seems to be a scarcity of tools that enable early-career professionals to invest in others' learning by bridging the earnings gap. Remember, earning also empowers us with freedom and the ability to live well. It is essential to recognize that earning and learning are not mutually exclusive but rather mutually reinforcing. When we have the financial runway to embrace empty time, we can seize opportunities for growth, prioritize learning, and unlock our potential.

Productively channelling empty time:

If we are able to channel empty time into other productive activities then we are able to ensure that the bar for staying in a job remains high as the opportunity cost is visible.

There are a few mechanisms that allow for this:

1. Learning backlogs: I tend to have a list of things that I’d like to learn at the next moment of empty time. These range from learning to code and nuances to AI models, to books that I’d like to read. Depending on the amount of empty time I have and how continuous it is, I will do one of these activities.

2. Activity backlog: I have a list of activities that are in a backlog (e.g. thesis writing, learning and creating new magic, appointments, getting better at skiing); these are beneficial but not urgent, so good for quiet moments

3. Bucket lists: I view bucket lists as living opportunities you’d like to do. If there are enough items on this list and enough friends to do them with then usually some these things should map to your empty time.

I view the activities in these backlogs to be worth quite a bit given the value I believe they will have on my life, making the bar for a job quite high.

Countering your own narrative

By keeping burn low to reduce the financial need for work, and productively channelling empty time we’re able to improve the bar that an existing job needs to meet to keep us there. The final challenge lies in the default bias, which often tempts us to stick with the status quo. It is easy to passively continue in our existing jobs, locations or fields. While stability has its merits, too often, people unwittingly harm their careers by neglecting change and growth.

It could be argued that CV gaps or ramp up time for new jobs may mean it is better to stay in a job - I’d disagree as although there may be some CV detriments associated with leaving a job without a replacement or career gaps, most individuals err on the side of caution, detrimentally stunting their professional progress. Having a way to leverage empty time should allow you to counter this CV gap issue, and more aggressively pursue growth.

Similarly, the ramp up time associated with starting a new to job may be more demanding than the existing job, and therefore reduce the ratio of reward to time committed. However, we often underestimate how much our current jobs prevent us from exploring new horizons. By making empty time productive, we liberate ourselves from suboptimal learning experiences, mediocre earnings, and diminished enjoyment. Embracing idle moments allows us to optimize outcomes and intentionally separate learning, earning, and enjoyment. Furthermore, this approach positions us to seize new opportunities when they arise without constraints of employment.

Thoughts on Time

One of the most curious things about time is that it is seen as an equaliser, but in actual fact its distribution is as unequal as many other resources. Uses of prior time affect the both the quantum and quality of future time. Moreover, everyday people trade time, so there is clear way in which money or other forms of value can help you better leverage time.

The unequal nature of time may make it feel less precious, however if anything it is this fact that makes it all the more precious.

High quantum low quality time is miserable, low quantum high quality time is probably better; high of both feels optimal.

Well used time compounds.

Half life on time

https://twitter.com/sama/status/910895248724828160?s=20

There are numerous learnings from this concept [detailed in this post], but most prevalently here; it’s important to see how this affects how you prioritise – learning, earning and living.

With time, earning ability may increase but ability to leverage time to learn or live decreases. I feel society overemphasises the importance of earning and saving when young (likely driven by insane asset value compounding the past 50 years), but not enough the importance of learning or living.

If time feels longer when you’re younger, I imagine the memories will also stay etched with us for longer and with higher strength. This suggests to me that the impact of living would be more substantial with memories etched from living earlier. Your health is also better when younger, and as such so is your ability to “live”. Living also has compounding effects. If you go to parties, you are more likely to meet more people, who invite you to more parties. If you become the kind of person who prioritises enjoyment, you are likely to find newer ways to create new kinds of enjoyment.

Similarly, learning is affected by your brains neural plasticity which reduces over time. However, this reduction in neural plasticity can also be reduced by ensuring a life of continual learning. People who know two languages, can much more easily learn a third, as both the learning process is very similar, but also the rule learnt for both languages likely overlap with the third language. Similarly, the amount of overlap in learning of different things is exponential.

In all cases of learning, earning and living, the benefits compound. Don’t stray too far in prioritising one at the detriment of the other two.

Final thoughts…

Time is precious.

If you are privileged enough to not need to worry only about immediate financial need, then you should embrace this - optimally using time will yield dividends in the long run. Here I’ve provided one view of how to do this, there are many others.

Do not let time pass passively – actively live every moment. This essay gives an indication of how to assess time use and structure time effectively which should in turn lead to more active use of time.

Thanks for reading about my daydreams. If you enjoy them or have questions or suggestions please let me know in the comments. Would also love for you to subscribe and share with a friend who would enjoy them.

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Similarly, humans often perceive more future pain to be less bad than a lesser pain received earlier. Part of this is justified based on the possibility that the future pain will never come, but this effect exceeds that level.

Delaying pain leads to indecision, and indecision allows you to spend less time to intensely act as the aire of indecision lingers as you half act towards something.

Learning techniques to optimise for delaying gratification will almost certainly yield reward – if anything doing boring stuff well is one of the few things that school teaches us.

Where school fails, is that at school things are too formulaically bound by time rather than activity. Success in startups and life is often about working to outcomes over specific timescales.

A choice between two negative outcomes remains a choice.

Inaction is an action. Inaction in making a decision remains an action and is therefore a choice. Inaction in a decision between two negative outcomes therefore also remains a choice.

Every action leads to an allocation of mental or physical energy. The process of deciding an action also uses some mental or physical energy.

There exist situations where the decision-making process consumes more energy than the expected energy difference in the choices available.

In these situations, defaults are often used. The use of defaults however, remains a choice –that the expected value of using a default exceeds the value of making a choice plus the net gain of the action.

Note to self.

In “A changing world order”, Ray Dalio evidences to us, that:

500 years ago, that wealth accrued to the kings and landowners, who owned the assets needed for agriculture - the core means by which humans sustained themselves in that era.

During the industrial revolution, as more utility could be gained through the creation of new product using existing resources, wealth accrued both to those who owned the the machinery that allowed for the creation of more utility from existing resources and those who owned the resources that powered those factories (coal and oil fields).

As we entered the internet era, wealth accrued to those who owned the platforms on which people spent their time. The difference in this case, is that whereas in prior shifts of this kind, there were geographical restrictions that prevented the global monopolisation of industries, the advent of the internet broke those geographic barriers, and allowed for small parties to both reap substantial proportions of the reward for the products that they created, but also exercise a lot of control over these platforms that people spent a lot of time on.

There are few clearer examples of this than the groundbreaking progress to AI over the past years and in particular over the past few months, with GPT3 coming out in June 2020, then DALLE2 coming out in October 2022 allowing users to produce unique high quality images in seconds off of simple prompts, and chat-GPT3 releasing in November 2022 reaching over 1m users within 5 days.

*Stability and midjourney’s models have been equally impressive.

The combination of the power of the technology and the unprecedented pace of it’s development mean that it will be more disruptive to human livelihoods than any technology before it. Where other technological changes have led to a change in the type of work that humans have needed to do, AI has the ability to truly replace the need for human labour at all - and the time at which this change occurs appears to be getting nearer and nearer.

If AI reduces the need for human labour, in it’s current state there is likely to be a substantial increase in inequality and an acceleration in the need for things like universal basic income. However, given the way in which AI leverages the prior and current work of humans, there also should be a more equitable way to reward those who contribute to the outputs achieved.

Here I lay out a short case for the benefits of leveraging decentralised technology in the development of AI models.

Case Study: How Generative AI Works

Above is a really simple graphic of the core parts of an AI model. For a slightly more in depth view of this, see this post on how AI Models work. At each of these stages, current or prior human work is leveraged in order to create output:

1. Training / test data collated:

   The data / images used to train the model were likely generated by humans somewhere at some point - note, some cases of synthetic data may have reduced human contact.

2. Model architecture:

   The model would have been built by a team. For example, text-davinci-003 is OpenAI’s most capable GPT-3 model.

3. Prompt entered:

   Humans create prompts for the model to be trained on, or they create the system to automate prompt generation.

4. Output reviewed:

   Humans review the outputs of the model and assess for accuracy, precision and recall. This could be as simple as the model requesting feedback post the creation of the output.

5. Model parameters adjusted:

   Parameter adjustments systems are made to the model likely by the team who created the initial model.

Currently, the steps taken to identify and track the contributors to relevant outputs are either done in a centralised way or not at all. Whereas Stability AI have opensourced their dataset, OpenAI’s remains closed.

Emad Mostaque refers to how Apple have mechanisms for verified content creation. The interview also talks about how Stable Diffusion are partnering with Adobe on content authenticity - a small metadata file that attaches to every piece of content created and its hash. This allows for the detection of any edits made to the content.

This same technology can be used to label the creators of the content that is making up the training data, and to identify the individuals who review the outputs during the test phases that lead to adjusting parameters and a better result. Moreover, outputs can be attributed to all of the contributors who helped to produce them, from training data providers to prompt writers.

Why Blockchain

Leveraging blockchain in this case has a 3 core benefits:

1. Rails: Ease of reward distribution/attribution

2. Trust: at scale

3. Growth: Composability/Interoperability

Rails: Ease of reward distribution

Given transparency and sovereignty of asset ownership, transactions on blockchain remain one of the most efficient mechanisms for distributing rewards. Where paypal has substantial fees that they could change on a whim, and the ability to freeze or seize assets at whim, on chain payments do (*should) not. Where bank transfers are often limited by working hours and subject to heavy fees, blockchain transfers are available as long as there is a suitable party willing to validate the transaction at the relevant cost.

Though there are concerns with using blockchain compared to a centrally run alternative due to poorer UX, less efficient infrastructure and the difficulty rectifying issues as easily, longer term we anticipate substantial progress in infrastructure efficiency and UX which should in turn lead to fewer issues that need rectifying.

Trust: at scale

As adoption of AI grows exponentially, and the size of the inputs also grow, those who own parts of those means of production may be incentivised to train the models to encourage certain behaviour to their own benefit: this could range from pushing certain products all the way through to spreading misinformation to manipulate election results.

Putting this data on chain will allow for more trust at scale, as it will also allow individuals to test systems and identify exactly where problems may be occurring. For example, if there is clear bias in AI models, the training data used that caused the bias can be identified, but also new data can be added to rectify the model to counter the bias.

Growth: Composability / Interoperability

One of the other core benefits of using blockchain to map the components of an AI output is the ability to leverage components of the model more effectively and build on top of existing infrastructure. Leveraging certain training data with a different kind of model or specific scenario in a trusted way will allow for the space to grow faster. It also has huge benefits for finetuning of models.

Finetuning

As the core models improve their precision for broader use cases, over time users go through a process of finetuning - where individuals can add to the model using their own training data set.

This can be really good for application specific usage of AI, or to create a more personalised model for specific individuals or groups of people based on their geography, dialect or even profession - anything that would change dialect enough that a specific data set would improve accuracy of results.

Final Thoughts

Does the the non-profit nature of OpenAI change the need for this?
Can the same trust be built through well structured centralised systems?

Note: These are just random thoughts I have whilst daydreaming… don’t take them too seriously. Please subscribe, share with a friend or two, and let me know what sort of content you’d like to see next.

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Cool references:

Alliance DAO request for AI x Crypto related projects

https://avc.com/2022/12/sign-everything/

That’s it… won’t write much more on this, DC Investor has his view, I will write an explainer on my assessment of Luna soon… summary: Luna was ambitious and had some token of opportunity for the greater good… 20% anchor yields were a terrible idea, slow Luna growth with focus on expanding non-stable Luna L1 chain adoption and collaterilsation of UST may have given Luna a shot at success… but the risks were too high to be 100% in.

2) Optimism Airdrop

After an initial demand overload, the price of the token has fallen off a cliff.

New proposal to ban dumpers, though some of this is controversial, what I love about the optimism airdrop so far is how first principles it feels. The lack of governance rights, the hint at future airdrops and the retrospective airdrop changes optimise for building a solid web3 community on optimism over appeasing farmers

3) Solana Outage… again

I have mixed views on Solana. Yes the memes will rinse how they’re consistently down, however we’re still early. As long as the chain maintains it’s immutability outages are fixable. Hard forks and pulling back the chain are less acceptable.

SBF expressed his view in the 80,000 podcast - in summary though it may be mispriced, Solana is an impressive chain, that can do 50,000 TPS compared to 10 TPS on Eth.

Crypto mass adoption requires this level of TPS - especially as it enters the fields of social media, health care, education. So though I don’t own any, my view on Solana isn’t substantially affected by this outage alone.

4) Nate Chastain arrested for insider trading of NFTs

Nate ran product at Opensea, and was called out originally by @0xZuwu.

He was dismissed by OpenSea and a lot of people thought this was the end. However, clearly law enforcement had other ideas.

There are 2 sides to this spectrum. Some people view his actions as bad but relatively small compared to the scale of other scams and rug pulls in this space. Others view it as a good precedent to help clean up the space from a lot of the “Alpha” that people trade on.

Crypto + Climate tech = aka a VC dream… second time founder with a unicorn exit… even dreamier… founder left WeWork then continued to consult for them for a $185m fee (a contract that he then apparently violated)

If there’s anything Neumann did well it was to play VCs… WeWork even wrote a guide on how to do this in 2014

Notably absent from this round was Softbank, and on a cursory scan of Dealroom and Business Wire seemingly any of the other early investors in WeWork

2) A16Z raises it’s latest $4.5bn crypto fund

With $4.5bn to deploy, leading the FlowCarbon round makes sense, even just to justify the 2% management fee.

More seriously however, it does show that despite market turmoil and long term pessimism in the market, there is enough dry powder that needs to be deployed. All the more reason to build.

Size of the fund however does call into question just how much of web3 VCs own. Great responses in the Bankless podcast

3) Delphi released the merge report

Vitalik endorsed it so you can trust it’s accurate.

In other news the merge is coming… I’ll probably repost this and add snippits of more detail on why this is so huge as it becomes interesting… but the merge is coming.

Other Observations:

If you’re wanting to understand just how substantial the drop has been since the bull market, was looking into a punk this week. It was almost half it’s price in Eth a year ago yet much higher in USD… :,(

New attempts continue to take place, with Purdue university introducing their new student financing options tied to income share, and upstart institutions like Make and Lambda school financing new students through income share agreements, which are then sold as securities to investors. Lambda has recently come under scrutiny for violating California Consumer Financial Protection Law.

However, I believe the fundamentals, particularly for education are strong enough to make take-up at some point an inevitability.

Money now is more valuable to people than money later

a.    Incomes generally increase with age, disposable incomes are highest in the latter years of work when incomes are highest and cost of living is lowest

b.    Happiness achieved per incremental dollar is also much higher for those on lower incomes and caps off at a certain point ~$75,000

c.     Return on investment in education and early career development is high – both for intrinsic skill sets learned, as well as network, signalling and other value

d.    Experiences generally generate more happiness than things, and certain experiences are less easily attainable with age for obvious health reasons

Societal benefit

If the investment is skewed towards education and training - the outcomes also include a more skilled and educated workforce. If applied correctly this should correspond to proportionate increases in innovation, and therefore overall improvements in living standards.

Competition for returns ever-growing

Financial institutions are incentivised to find new ways of generating those returns – Friedman and Kuznets (1945) posed that return on investment for human capital was higher than for physical capital

Invest in everything economy growing

As securitisation becomes easier, the ability to invest in different things also becomes easier, from whisky, to art, to music masters

People invest more than they give to charity

Americans donated a total $449.64bn in 2019, 69% of which was from individuals, in comparison, the US stock market had $37.7T invested in it in 2019. Investment that improves prospects for the investee is better if it improves their overall outcomes.

Student debt crisis growing

The actual student debt burden stands above car and credit card debt at $1.6T in 2019 and it is growing at 7% per year, expected to exceed $2T in 2021 and $3T by 2030.

There is an additional mental and physical health burden that has long-term negative impacts on the economy and workforce productivity, not to mention happiness

Additional benefit to human capital investment

The best investors are those who are either super active or completely passive. From active investors, investees will benefit from network access, mentorship, and other decision-making advice, all of which should positively contribute to their earning potential and impact on society

Some fields cannot access debt

Fields such as the creator economy have large groups of people who cannot easily access debt, despite having large amounts of name recognition and decently sized fan bases. Income share agreements can allow people in these high risk and unstable fields to stabilise their incomes and also leverage their fan bases.

Pooled income

Alternatively, this stabilisation can even be done through schemes like pooled income, for example where everyone in the BRIT school class of 2021 can agree to pool 10% of their income, allowing the entire class to try to take more risk in an industry where revenue is concentrated around the 1%. If the class produces an Adele, then the whole class benefits. The rest of the class is the incentivised to help eachother too succeed whilst also having the stability of multiple salaries.

Question

How will the increased adoption of income share agreements affect inequality?

Though at the start these agreements are often being structured as a meaningful upfront cash investment for a small proportion (5-10%) of future earnings limited to a certain number of years post investment, what’s to stop an escalation where investors invest smaller amounts for a more substantial stake (~90%) in earnings?

A classic response to this argument would be that the freemarket would realise that this sort of deal will not create outsized rewards for investors and so the freemarket would not let this happen as it would disincentivise people to work. I fundamentally disagree for 2 reasons:

1. There are multiple instances where investors have aggressively taken substantial stakes in companies enough so to disincentivise further founder work on the companies? In these instances they have refused to recap or lower their stakes even if this directly led to the future collapse of the company. In the investment game they are often fine to trash a bunch of companies to have a substantial stake in a unicorn. The issue here is that these investments are in people, not companies, and taking too substantial a stake in a human in return for early up front cash seems not dissimilar to modern day slavery.

2. As society progresses, this sort of investment funding provision that starts out as useful for non-essential items, such as further education or a house (to reduce the stress of paying a mortgage to encourage entrepreneurship) can progress to more essential items. Let’s say for example that everyone around you takes up this financing for early education. As a result everyone else also feels the need to take up this provision so as to not be disadvantaged. This will lead to demand for private education going up, leading to price rises, and state education provision likely decreasing as the resources flood to private education. Over time this could lead to a deterioration in the quality of state education to such an extent that private education becomes necessary for access to even the most basic jobs. If investment doesn’t scale at the same rate then people will be giving more away. Decisions that you made as a kid could affect you for life.

Moreover, this free market approach will likely exacerbate existing trends where demand for investment space in top talent will bid up that price, but for the majority there will not be much funding at all, so the cost of capital goes up!

Assuming that these sorts of income share agreements are going to scale, where should regulation be put in place:

1. Max investment at each stage?

2. Necessary state training provision?

3. Minimum age before investment?

4. somewhere else?

As always, please do reach out with comments and feedback or feel free to email or message me on LinkedIn or any socials.

For most of you, these are the first writings of mine that you’ll be reading. Those of you who knew me aged 15 will know my last blog, when my daydreams were generally about how I could no longer fit into a box, or how I was procrastinating from revising by writing a blog about procrastinating from revising (a phrase I now know to be “meta”). These daydreams would then go into tangents about the rest of life… a bit like my actual daydreams.

Now, 8 years, 2 schools, a university, a gap period in India, several internships in startups, M&A and PE, and (literally) becoming a magician, later, my daydreams have shifted a bit… and I’ve learnt to create a new blog post linked with a hyperlink whenever I go on a tangent. However, hopefully, the value remains, either informationally, comically, or just from a standpoint of being genuinely curious about what I think about all day.

Purpose

This blog expresses my views or hypothetical thought process at the point in time when those words are written. The purpose is three-fold, to:

1. (Hopefully) provide something of informational value and intrigue

2. Get feedback on ideas discussed, both from those who agree and those who disagree

3. Interact with people who are interested

Additionally, I am writing:

1. To force myself to put thoughts on paper, both for cataloguing and reference purposes

2. To become a better writer through practice

3. For journaling purposes, to stop my brain from spinning on certain ideas

The purpose is not in any way to provide a definitive view of the world, nor to give any binding advice, especially on my views that are related to finance.

Views being expressed are entirely my own and not of my employer or the like.

A bit more about myself

I’m 23, live in north London, studied Psychology & Philosophy at Oxford before becoming a management consultant – where I spent the first year travelling the world solving awesome problems, and the second year travelling the 1.5m between my bed and my desk until I forgot what day of the week it was.

I’ve also spent the last 9 years creating and performing magic (www.magicromil.com), you may have seen me on your TV on Sky Sports or Star, or spammed on the Instagram and Snapchat feeds of your favourite creators!

I now work in the Web3 space, for a start-up that’s yet to launch, but still very cool. We work with brands and talent to launch community tokens - with a rockstar team, solid market positioning, and tailwinds as talent and brands pivot into web3.

When I’m not doing all of this, I daydream a lot about the creator economy, web3 psychology, philosophy and general oddities of the world. Within the creator economy, I daydream a lot about the creator middle class and how to redress power and knowledge imbalances within the industry to bring more power to creators. Within Web3 I daydream about the impact of decentralisation on platforms, especially related to the creator economy, education and mental health. Within psychology, I daydream about neurodiversity, mental health and the overall talent/education ecosystem. Within Philosophy I daydream about ethics, utilitarianism, effective altruism and how Web3 fits with those views. Within general oddities of the world, expect material about things that are astoundingly obvious when made explicit.

I can be reached on socials @magicromil, or LinkedIn Romil Depala… so please do reach out… if there’s anyone who’s also up for pre-reading my posts before they’re released both to make sure it makes sense and provide some early critique please do message

Happy reading 😊